Detecting object pose using autoencoders

ABSTRACT

Methods and a system are provided for detecting object pose. A method includes training, by a processor, a first autoencoder (AE) to generate synthetic output images based on synthetic input images. The method further includes training, by the processor, a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images. The method also includes training, by the processor, a neural network (NN) to detect the object pose using the synthetic output images generated by the first and second AEs. The method additionally includes detecting and outputting, by the processor, a pose of an object in a real input test image by inputting the real input test image to the second AE to generate a synthetic image therefrom, and inputting the synthetic image to the NN to generate an NN output indicative of the pose of the object.

The following disclosure(s) are submitted under 35 U.S.C. §102(b)(1)(A):

DISCLOSURE(S): “Transfer learning from synthetic to real images usingvariational autoencoders for robotic applications”, Tadanobu Inoue,Subhajit Chaudhury, Giovanni De Magistris, and Sakyasingha Dasgupta,Sep. 20, 2017, https://arxiv.org/abs/1709.06762 andhttps://youtu.be/Wd-1WU8emkw.

BACKGROUND Technical Field

The present invention relates generally to information processing and,in particular, to detecting object pose using autoencoders (AEs).

Description of the Related Art

Large labeled datasets are important for training deep neural networks(DNNs). However, preparing many labeled real images is expensive andtime-consuming.

Synthesizing labeled images for preparing training data has becomeappealing. However, learning from synthetic images may not achieve thedesired performance in real environments due to, for example, a gapbetween synthetic and real images. Hence, there is a need for animproved approach to synthesize labeled images to make them more similarto corresponding real images.

SUMMARY

According to an aspect of the present invention, a computer-implementedmethod is provided for detecting object pose. The method includestraining, by a processor, a first autoencoder (AE) to generate syntheticoutput images based on synthetic input images. The method furtherincludes training, by the processor, a second AE to generate syntheticoutput images, similar to the synthetic output images generated by thefirst AE, based on real input images. The method also includes training,by the processor, a neural network (NN) to detect the object posecorresponding to a simulated environment using the synthetic outputimages generated by the first AE and the second AE. The methodadditionally includes detecting and outputting, by the processor, a poseof an object in a real input test image by inputting the real input testimage to the second AE to generate a synthetic image therefrom, and theninputting the synthetic image to the NN to generate an NN outputindicative of the pose of the object corresponding to an actualenvironment.

According to another aspect of the present invention, acomputer-implemented method is provided for detecting object pose. Themethod includes training, by a processor, a first autoencoder (AE) togenerate synthetic output images based on synthetic input images. Themethod further includes training, by the processor, a second AE togenerate synthetic output images, similar to the synthetic output imagesgenerated by the first AE, based on real input images. The method alsoincludes training, by the processor, a multi-layer perceptron (MLP) todetect object pose corresponding to a simulated environment using onlyencoder outputs and bypassing decoder outputs of the first AE. Themethod additionally includes detecting and outputting, by the processor,a pose of an object in a real input test image by inputting the realinput test image to an encoder of the second AE to generate an encodedsynthetic image therefrom, and inputting the encoded synthetic image tothe MLP to generate an MLP output indicative of the pose of the objectcorresponding to an actual environment.

According to yet another aspect of the present invention, a system isprovided for detecting object pose. The system includes a processor. Theprocessor is configured to train a first autoencoder (AE) to generatesynthetic output images based on synthetic input images. The processoris further configured to train a second AE to generate synthetic outputimages, similar to the synthetic output images generated by the firstAE, based on real input images. The processor is also configured totrain a neural network (NN) to detect the object pose corresponding to asimulated environment using the synthetic output images generated by thefirst AE and the second AE. The processor is additionally configured todetect and output a pose of an object in a real input test image byinputting the real input test image to the second AE to generate asynthetic image therefrom, and then inputting the synthetic image to theNN to generate an NN output indicative of the pose of the objectcorresponding to an actual environment.

These and other features and advantages will become apparent from thefollowing detailed description of illustrative embodiments thereof,which is to be read in connection with the accompanying drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

The following description will provide details of preferred embodimentswith reference to the following figures wherein:

FIG. 1 is a block diagram showing an exemplary processing system towhich the invention principles may be applied, in accordance with anembodiment of the present invention;

FIGS. 2-3 are flow diagrams showing an exemplary method for detectingobject position using variational autoencoders (VAEs), in accordancewith an embodiment of the present invention;

FIG. 4 is a block diagram graphically showing some of the steps/blocksof the method of FIGS. 2-3, in accordance with an embodiment of thepresent invention;

FIG. 5 is a flow diagram further showing aspects of a step/block andanother step/block of the method of FIGS. 2-3, in accordance with anembodiment of the present invention;

FIG. 6 is a block diagram further graphically showing aspects of astep/block and another step/block of the method of FIGS. 2-3, inaccordance with an embodiment of the present invention;

FIGS. 7-8 are flow diagrams showing an exemplary method for detectingobject position using variational autoencoders (VAEs), in accordancewith an embodiment of the present invention;

FIG. 9 is a block diagram graphically showing some of the steps/blocksof the method of FIGS. 7-8, in accordance with an embodiment of thepresent invention; and

FIG. 10 is a block diagram showing network structures to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention.

DETAILED DESCRIPTION

The present invention is directed to detecting object pose (e.g.,positions and/or angles), using autoencoders (AEs). For the sake ofillustration, one or more embodiments herein may be described withrespect to variational autoencoders. However, it is to be appreciatedthat the present invention can utilize any type of autoencoderincluding, for example, but not limited to, deep spatial autoencoders,variational autoencoders, and so forth, as readily appreciated by one ofordinary skill in the art given the teachings of the present inventionprovided herein, while maintaining the spirit of the present invention.

In an embodiment, the present invention uses two variationalautoencoders (VAEs) configured to generate similar images from syntheticand real images for the purpose of detecting object position. To thatend, in an embodiment, the present invention can make generated trainingimages and generated test images be similar to each other. In anembodiment, non-realistic blurry images can be allowed on VAE outputs.In an embodiment, the present invention can provide improved detectingperformance over conventional approaches while using a significantlysmall real image dataset.

Advantageously, the present invention can be used to provide nearhuman-level control in robotics for specific tasks, to name one of amyriad of possible scenarios and applications to which the presentinvention can be applied, as readily appreciated by one of ordinaryskill in the art given the teachings of the present invention providedherein, while maintaining the spirit of the present invention.

These and other features of the present invention are described infurther detail herein below.

FIG. 1 is a block diagram showing an exemplary processing system 100 towhich the invention principles may be applied, in accordance with anembodiment of the present invention. The processing system 100 includesat least one processor (CPU) 104 operatively coupled to other componentsvia a system bus 102. A cache 106, a Read Only Memory (ROM) 108, aRandom Access Memory (RAM) 110, an input/output (I/O) adapter 120, asound adapter 130, a network adapter 140, a user interface adapter 150,and a display adapter 160, are operatively coupled to the system bus102. At least one Graphics Processing Unit (GPU) 194 is operativelycoupled to the system bus 102.

A first storage device 122 and a second storage device 124 areoperatively coupled to system bus 102 by the I/O adapter 120. Thestorage devices 122 and 124 can be any of a disk storage device (e.g., amagnetic or optical disk storage device), a solid state magnetic device,and so forth. The storage devices 122 and 124 can be the same type ofstorage device or different types of storage devices.

A speaker 132 is operatively coupled to system bus 102 by the soundadapter 130. A transceiver 142 is operatively coupled to system bus 102by network adapter 140. A display device 162 is operatively coupled tosystem bus 102 by display adapter 160.

A first user input device 152, a second user input device 154, and athird user input device 156 are operatively coupled to system bus 102 byuser interface adapter 150. The user input devices 152, 154, and 156 canbe any of a keyboard, a mouse, a keypad, an image capture device, amotion sensing device, a microphone, a device incorporating thefunctionality of at least two of the preceding devices, and so forth. Ofcourse, other types of input devices can also be used, while maintainingthe spirit of the present invention. The user input devices 152, 154,and 156 can be the same type of user input device or different types ofuser input devices. The user input devices 152, 154, and 156 are used toinput and output information to and from system 100.

Of course, the processing system 100 may also include other elements(not shown), as readily contemplated by one of skill in the art, as wellas omit certain elements. For example, various other input devicesand/or output devices can be included in processing system 100,depending upon the particular implementation of the same, as readilyunderstood by one of ordinary skill in the art. For example, varioustypes of wireless and/or wired input and/or output devices can be used.Moreover, additional processors, controllers, memories, and so forth, invarious configurations can also be utilized as readily appreciated byone of ordinary skill in the art. These and other variations of theprocessing system 100 are readily contemplated by one of ordinary skillin the art given the teachings of the present invention provided herein.

FIGS. 2-3 are flow diagrams showing an exemplary method 200 fordetecting object pose using variational autoencoders (VAEs), inaccordance with an embodiment of the present invention. FIG. 4 is ablock diagram graphically showing some of the steps/blocks of the method200 of FIGS. 2-3, in accordance with an embodiment of the presentinvention.

In the embodiments of FIGS. 2-4, the method 200 involves a variationalautoencoder (hereinafter “VAE”) 410 formed from a first generativeneural network (NN) VAE (hereinafter “VAE1”) 411, a second generative NNVAE 412 (hereinafter “VAE2”). The method 200 further involves a NN 420.VAE1 411 includes an encoder 411A and a decoder 415. VAE2 412 includesan encoder 412A and a decoder 415. VAE1 411 and VAE2 412 have distinctencoder layers, but have the same decoder. In the embodiments of FIGS.2-4, the method 200 is used to detect object pose using VAE1 411 andVAE2 412, which are configured to generate similar images from syntheticand real images. Hence, method 200 detects a real object pose from rawRGB-D (Red, Green, Blue, depth) image data. In other embodiments, theimage data can be gray scale image, RGB color image, and only depthdata, in addition to RGB-D data.

In an embodiment, method 200 includes blocks 210 through 230. In anembodiment, blocks 210 through 220 correspond to a training phase 291 ofmethod 200, while block 230 corresponds to a testing phase 292 of method200.

At block 210, configure VAE1 411 and VAE2 412 to generate similar (e.g.,common) images from synthetic input images 401 and real input images402. The synthetic input images 401 and the real input images 403 usedfor block 210 are labeled images and can form image pairs as describedbelow.

In an embodiment, block 210 can include one or more of blocks 210A-210C.

At block 210A, set up a simulation environment that looks similar to thereal world or portion of interest thereof and capture large-scalesynthetic images along with corresponding ground-truth object poselabels.

At block 210B, train VAE1 411 to reconstruct the same synthetic imagesfrom an input such that VAE1 411 generates synthetic-like output images(hereinafter interchangeably referred to as “synthetic output images” inshort) 402 from the synthetic input images 401. That is, thesynthetic-like output images 402 can be blurry and different fromoriginal synthetic input images 401, but they look similar. Block 210Bcorresponds to “synthetic-to-synthetic” image learning.

At block 210C, train VAE2 412 to generate synthetic output images 404 inwhich a synthetic object is located at the same pose corresponding tothe real images such that VAE2 412 generates synthetic-like outputimages (hereinafter interchangeably referred to as “synthetic outputimages” in short) 404 from the real input images 403. Block 210Ccorresponds to “real-to-synthetic” image learning. In an embodiment(during block 210C), the decoder 415 of VAE2 412 can be fixed to keepthe outputs (synthetic-like images 404) of VAE2 412 to be similar to theoutputs (synthetic-like images 402) of VAE1 411 (again noting that VAE1411 and VAE2 412 have the same decoder 415, despite having differentencoder layers 411A and 412A, respectively). As used herein, the phrasefixing the decoder refers to fixing neural network weights duringtraining. Usually we don't fix neural network weights during trainingand then we can obtain neural network weights to output expectedresults. At this step in this embodiment, we do not change the weightsof the decoder and concentrate the training on the weights of theencoder. As used herein, similar phrases refer to the similarity of twooutput images from VAE1 411 and VAE2 412. The two VAEs 411 and 412 usethe same decoder 415 and output similar images which are synthetic-likecan be a little bit blurry.

At block 220, train NN 420 to detect object pose (e.g., Px, Py) 406using the blurry synthetic-like output images 402 generated by VAE1 411.The detected object pose 406 is the object pose in the simulationenvironment shown in synthetic input image 401, relative to the detectedobject pose provided by the NN output 409 (with the NN output 409 beingthe object pose in the real world shown in real input image 407).

At block 230, detect a pose of an object in a real input test image 407by inputting the real input test image 407 to VAE2 412 to generate asynthetic-like image (hereinafter interchangeably referred to as“synthetic output image” in short) 408 therefrom, and then input thesynthetic image 408 to the NN 420 to generate an NN output 409indicative of the pose of the object in the real world (i.e.,corresponding to an actual environment versus a simulated environment).In an embodiment, the NN output 409 can be provided, depending upon theimplementation, as a pair (Px, Py) or 6 position and angle data (Px, Py,Pz, Rx, Ry, Rz), each value representing a particular dimensional valuerelating to the pose of the object. For example, in an embodiment, theNN output 407 can be provided as “(Px, Py)”, corresponding to the x andy dimensions, respectively. In another embodiment, which may relate anarea such as, but not limited to robotics, the “pose” informationprovided by the present invention can include one or more of positions(Px, Py, Pz) and/or one or more of angles (Rx (roll), Ry (pitch), Rz(yaw), depending upon the implementation.

FIG. 5 is a flow diagram further showing aspects of step/block 210 andstep/block 230 of method 200 of FIGS. 2-3, in accordance with anembodiment of the present invention. FIG. 6 is a block diagram furthergraphically showing aspects of step/block 210 and step/block 230 ofmethod 200 of FIGS. 2-3, in accordance with an embodiment of the presentinvention.

At block 510, corresponding to the training phase 291, place the objectat a grid position in a training area 631, during the training phase toachieve a uniform distribution of object position.

At block 520, corresponding to the testing phase 292, evaluate theobject at a random position in a testing area 632. We perform domaintransfer from synthetic environment to a real one by using the abovetrained NN, in order to detect objects placed at random positions.

It is to be appreciated that the training area 631 and the testing area632 can be of the same size.

FIGS. 7-8 are flow diagrams showing an exemplary method 700 fordetecting object pose using variational autoencoders (VAEs), inaccordance with an embodiment of the present invention. FIG. 9 is ablock diagram graphically showing some of the steps/blocks of the method700 of FIGS. 7-8, in accordance with an embodiment of the presentinvention.

In the embodiments of FIGS. 7-9, the method 700 involves a variationalautoencoder (hereinafter “VAE”) 410 formed from a first generativeneural network (NN) VAE (hereinafter “VAE1”) 411, a second generative NNVAE 412 (hereinafter “VAE2”), similar to method 200 of FIGS. 2-3. Themethod 700 further involves a multi-layer perceptron (MLP) 920. VAE1 411includes an encoder 411A and a decoder 415. VAE2 412 includes an encoder412A and a decoder 415. VAE1 411 and VAE2 412 have distinct encoderlayers, but have the same decoder. In the embodiments of FIGS. 7-9, themethod 700 is used to detect object pose using VAE1 311 and VAE2 412,which are configured to generate similar images from synthetic and realimages. Hence, method 700 detects a real object pose from raw RGB-D(Red, Green, Blue, depth) image data. In other embodiments, the imagedata can be gray scale image, RGB color image, and only depth data, inaddition to RGB-D data.

In an embodiment, method 700 includes blocks 710 through 730. In anembodiment, blocks 710 through 720 correspond to a training phase 791 ofmethod 700, while block 730 corresponds to a testing phase 792 of method700.

At block 710, configure VAE1 311 and VAE2 312 to generate similar (e.g.,common) images from synthetic input images 401 and real input images402. The synthetic input images 401 and the real input images 403 usedfor block 710 are labeled images and can form image pairs as describedbelow.

In an embodiment, block 710 can include one or more of blocks 710A-710C.

At block 710A, set up a simulation environment that looks similar to thereal world or portion of interest thereof and capture large-scalesynthetic images along with corresponding ground-truth object poselabels. .

At block 710B, train VAE1 411 to reconstruct the same synthetic imagesfrom an input such that VAE1 411 generates synthetic-like output images(hereinafter interchangeably referred to as “synthetic output images” inshort) 402 from the synthetic input images 401. That is, thesynthetic-like output images 402 can be blurry and different fromoriginal synthetic input images 401, but they look similar. Block 710Bcorresponds to “synthetic-to-synthetic” image learning.

At block 710C, train VAE2 412 to generate synthetic output images 404 inwhich a synthetic object is located at the same pose corresponding tothe real images such that VAE2 412 generates synthetic-like outputimages (hereinafter interchangeably referred to as “synthetic outputimages” in short) 404 from the real input images 403. Block 710Ccorresponds to “real-to-synthetic” image learning. In an embodiment(during block 710C), fix the decoder 415 of VAE2 412 to keep the outputs(synthetic images 404) of VAE2 312 to be similar to the outputs(synthetic images 402) of VAE1 411 (again noting that VAE1 411 and VAE2412 have the same decoder 415, despite having different encoder layers411A and 412A, respectively). An example of how the decoder is fixed isdescribed above with respect to step 210C.

At block 720, train MLP 920 to detect object pose 906 corresponding to asimulated environment using only encoder outputs 902 (and not decoderoutputs) of VAE1 encoder 411A. That is, train the MLP 920 using only theencoder outputs 902 of VAE1 encoder 411A so as to bypass the decoderoutputs of VAE1 411. It is to be appreciated that the training time ofblock 720 will be shorter than the training time of block 220 of method200.

At block 730, detect a pose of an object in a real input test image 407by inputting the real input test image 407 to the encoder of VAE2 412(while bypassing the decoder of VAE2 412) to generate an encodedsynthetic-like image (hereinafter interchangeably referred to as“synthetic output image” in short) 908 therefrom, and then input theencoded synthetic image 908 to the MLP 920 to generate an MLP output 909indicative of the pose of the object in the real world (i.e.,corresponding to an actual environment versus a simulated environment).In an embodiment, the MLP output 909 can be provided, depending upon theimplementation, as a pair (Px, Py) or 6 position and angle data (Px, Py,Pz, Rx, Ry, Rz), each value representing a particular dimensional valuerelating to the position of the object. For example, in an embodiment,the MLP output can be provided as “(Px, Py)”, corresponding to the x andy dimensions, respectively. In another embodiment, which may relate anarea such as, but not limited to robotics, the “pose” informationprovided by the present invention can include one or more of positions(Px, Py, Pz) and/or one or more of angles (Rx (roll), Ry (pitch), Rz(yaw), depending upon the implementation.

FIG. 10 is a block diagram showing network structures 1000 to which thepresent invention can be applied, in accordance with an embodiment ofthe present invention.

The network structures 1000 relate to a VAE 1001 and include an encodernetwork 1010, a mean vector 1020, a standard deviation vector 1030, asampled latent vector 1040, and a decoder network 1050.

The encoder network 1010 receives an input image 1001 (e.g., 400×200×4),and the decoder network 1050 outputs an output image 1002 (e.g.,400×200×4).

In an embodiment, the encoder network 1010 is formed from one or moreconvolutional neural networks. In an embodiment, the decoder network1050 is formed from one or more deconvolutional neural networks. Ofcourse, other types of neural networks and/or learning structures canalso be used in accordance with the teachings of the present invention,given the teachings of the present invention provided herein, whilemaintaining the spirit of the present invention.

The network structures 1000 further relate to a CNN 1060 having an RGBprocessing portion 1060A for receiving and processing RGB data and adepth processing portion 1060B for receiving and processing depth datato output position data (e.g., (Px, Py).

The network structures further relate to a MLP 1070 for receiving andprocessing a sampled latent vector 1040 to output position data (e.g.,(Px, Py).

Any of the networks 1010, 1050, and 1060 (noting that an MLP is a typeof (feedforward) neural network) can include one or more of thefollowing sets of layers: a set of convolutional layers; a set of maxpooling layers; a set of fully connected layers; and a set ofup-sampling layers. In any event, the MLP 1070 will include at least 3layers, namely an input layer, an output layer, and one or more hiddenlayers.

A further description will now be given regarding various aspects of thepresent invention, in accordance with one or more embodiments of thepresent invention.

The description will commence by more fully stating a problem to whichthe present invention is applied, followed by a description of variousaspects of the present invention relating to object position detectionusing VAEs.

When we have two labeled image datasets (one synthesized in a simulationenvironment, and the other captured in the real world), we can assumeimage instances as X_(S)={x_(S) ^(i)}_(i=1:N) and X_(R)={x_(R)^(i)}_(i=1:M) with S representing synthetic image data and Rrepresenting real image data, respectively. Since it is easy tosynthesize many images for expected labels in a simulation environmentand expensive to capture many images for expected labels in the realworld, typically M<<N. We thus aim to extract meaningful information,y_(R) ^((i))=f(x_(R) ^(i)), from the real world images that we can usefor subsequent tasks of interest.

However, due to time and cost constraints, it is difficult to collectsufficiently large amounts of real world images to guarantee asymptoticconvergence of the function of our interest, i.e., f. We take anapproach of modeling a given scene within a simulation environment inorder to learn the function mapping, y_(S) ^((i))=f(x_(S) ^(i)). This isdone based on a large amount of corresponding synthetic images which canbe collected easily in simulation. Given this setting, we want to learna conditional distribution of synthetic images given the real worldimages, p(x_(S)|x_(R)), by minimizing the following error:

L=

_(p)(x _(S) |x _(R))(∥f(x _(R))−f(x _(S))∥²)   (1)

where the expectation with respect to the conditional distributionminimizes the distance between the feature maps obtained from the realimages and the feature maps of the corresponding reconstructed syntheticimages obtained from the real images based on the conditionaldistribution p(x_(S)|x_(R)).

Herein, we focus on detecting real object positions from raw RGB-D (red,green, blue, depth) image data using this formulation as our target taskfor evaluating transfer learning. First, we train deep neural networks(DNN) with a large number of synthetic image data as well as a smallnumber of real image data along with their corresponding object positioninformation. In order to prepare the data for training, we assume theobject is put at a grid position during the training phase to achieve auniform distribution of object position as shown on the left side ofFIG. 6 corresponding to training phase 291. At the time of inference, asshown on the right side of FIG. 6 corresponding to testing phase 292, weperform domain transfer from a synthetic environment to a real one byusing the above trained DNN, in order to detect objects placed at randompositions.

We now further describe detecting object positions using VAEs, inaccordance with one or more embodiments of the present invention.

FIG. 4 mentioned above graphically depicts an embodiment of the presentinvention. An underlying principle of this approach is that thedistribution of image features may vary between simulated and realenvironments, but the output labels, like object position, should remaininvariant for the same scene. We use two VAEs for generating similarcommon images from synthetic and real image data and use this commondata distribution to train a convolutional neural network (CNN) topredict the object position with improved accuracy. Note that althoughwe use two VAEs with distinct encoder layers as generative models forimages, they have the same decoder, which is used to train the CNN.Thus, even if the VAE generates blurry images, the ensuing CNN willlearn to predict from this skewed but common image distribution. Sincethe CNN can be trained with many generated images from the syntheticdomain, we can achieve improved object position estimation from a verylimited set of labeled real images.

At least FIGS. 2-4 below respectively describe and show the stepsinvolved in an embodiment of the present invention. These steps caninclude the following, stated generally as:

-   (a) prepare two VAEs that output pseudo-synthetic images from both    synthetic and real images;-   (b) train a CNN to detect object positions using the output of    trained VAE in (a); and-   (c) detect object positions by transforming domains using the    trained VAE and CNN.

First, we prepare two VAEs to generate similar images from synthetic andreal images. We set up a simulation environment that looks similar tothe real world and capture large-scale synthetic images {x_(S)^(i)}_(i=1:N) along with corresponding ground-truth object positionlabels, {(t_(x) ^(i), t_(y) ^(i))}_(i=1:N). We train VAE1, which encodesand decodes from a synthetic image to the same synthetic image.

The encoder compresses the input image to the latent representations z,and the decoder reconstructs the image back from this latent space.However, using the encoder-decoder results in an intractable posteriordistribution p(z|x_(S)), so we optimize the encoder parameters byvariational inference and decoder parameters by minimizing the negativelog likelihood of the data. Using this method, we obtain the optimalparameters (θ,φ) by minimizing the lower bound given as follows:

_(S)(θ,φ;x _(S) ^(i))=D _(KL)(q _(φ) ^(S)(z ^(i) |x _(S) ^(i))∥p _(θ)(z^(i)))−

(log p _(θ)(x _(S) ^(i) |z))   (2)

where p_(θ)(z^(i)) is the prior distribution of the latentrepresentation, which is typically the Gaussian with zero mean and unitvariance.

We copy the weights of VAE1 to a VAE that has the same structure (VAE2)and then train VAE2, which encodes and decodes from a real image to thecorresponding synthetic image as graphically shown in FIG. 4. During thetraining, we fix the decoder layers and adapt only the parameters forthe encoder part, which receives the real images as input, correspondingto the conditional distributiong q_(β) ^(R)(z|x_(R)) with encoderparameters β. This is equivalent to forcing the latent space obtainedfrom the synthetic and real images to be identical. We obtain theoptimal parameter by minimizing the following lower bound:

_(R2S)(β;x _(S) ^(i))=D _(KL)(q _(β) ^(R)(z ^(i) |x _(R) ^(i))|p _(θ)(z^(i)))−

(log p _(θ)(x _(S) ^(i) |z))   (3)

In the above optimization, note that (x_(S) ^(i), x_(R) ^(i)) arematching pairs of corresponding synthetic and real images. The learnedencoder, q_(β) ^(R)(z|x_(R)), and decoder, p_(θ)(x_(S) ^(i)|z), can becombined to obtain the desired conditional distribution p(x_(S)|x_(R)),which can generate pseudo-synthetic images as output from thecorresponding real image as input. VAE2 outputs can be subsequently usedto obtain accurate object positions from a CNN trained purely in thesynthetic image domain.

Next, we train a CNN for detecting object positions as graphically shownin FIG. 4. Due to the availability of a large training datasetsynthesized in a simulation environment, we can obtain a good predictionby a trained CNN for detecting object positions. To overcome the gapbetween synthetic and real images, we use the outputs of the trainedVAEs in the above step, instead of using synthetic images directly.

Finally, we can detect object positions in the real world as shown inFIG. 4. VAE2 outputs blurry pseudo-synthetic common images, and the CNNtrained with the similar common images outputs object position.

There can be alternate strategies in steps (b) and (c), for example, asdescribed above relative to at least FIGS. 7-9. In the abovedescription, we train the CNN by using VAE1 output, but we can alsotrain a multilayer perceptron (MLP) by using the latent representationsobtained from the encoder of VAE1 in step (b). Since VAE1 and VAE2 havesimilar latent space structures, we can use latent space output fromVAE2 combined with the above trained MLP to detect object position.

These and other variations of the present invention are readilydetermined by one of ordinary skill in the art, given the teachings ofthe present invention provided herein, while maintaining the spirit ofthe present invention.

The present invention may be a system, a method, and/or a computerprogram product at any possible technical detail level of integration.The computer program product may include a computer readable storagemedium (or media) having computer readable program instructions thereonfor causing a processor to carry out aspects of the present invention.

The computer readable storage medium can be a tangible device that canretain and store instructions for use by an instruction executiondevice. The computer readable storage medium may be, for example, but isnot limited to, an electronic storage device, a magnetic storage device,an optical storage device, an electromagnetic storage device, asemiconductor storage device, or any suitable combination of theforegoing. A non-exhaustive list of more specific examples of thecomputer readable storage medium includes the following: a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), a static random access memory (SRAM), a portablecompact disc read-only memory (CD-ROM), a digital versatile disk (DVD),a memory stick, a floppy disk, a mechanically encoded device such aspunch-cards or raised structures in a groove having instructionsrecorded thereon, and any suitable combination of the foregoing. Acomputer readable storage medium, as used herein, is not to be construedas being transitory signals per se, such as radio waves or other freelypropagating electromagnetic waves, electromagnetic waves propagatingthrough a waveguide or other transmission media (e.g., light pulsespassing through a fiber-optic cable), or electrical signals transmittedthrough a wire.

Computer readable program instructions described herein can bedownloaded to respective computing/processing devices from a computerreadable storage medium or to an external computer or external storagedevice via a network, for example, the Internet, a local area network, awide area network and/or a wireless network. The network may comprisecopper transmission cables, optical transmission fibers, wirelesstransmission, routers, firewalls, switches, gateway computers and/oredge servers. A network adapter card or network interface in eachcomputing/processing device receives computer readable programinstructions from the network and forwards the computer readable programinstructions for storage in a computer readable storage medium withinthe respective computing/processing device.

Computer readable program instructions for carrying out operations ofthe present invention may be assembler instructions,instruction-set-architecture (ISA) instructions, machine instructions,machine dependent instructions, microcode, firmware instructions,state-setting data, or either source code or object code written in anycombination of one or more programming languages, including an objectoriented programming language such as SMALLTALK, C++ or the like, andconventional procedural programming languages, such as the “C”programming language or similar programming languages. The computerreadable program instructions may execute entirely on the user'scomputer, partly on the user's computer, as a stand-alone softwarepackage, partly on the user's computer and partly on a remote computeror entirely on the remote computer or server. In the latter scenario,the remote computer may be connected to the user's computer through anytype of network, including a local area network (LAN) or a wide areanetwork (WAN), or the connection may be made to an external computer(for example, through the Internet using an Internet Service Provider).In some embodiments, electronic circuitry including, for example,programmable logic circuitry, field-programmable gate arrays (FPGA), orprogrammable logic arrays (PLA) may execute the computer readableprogram instructions by utilizing state information of the computerreadable program instructions to personalize the electronic circuitry,in order to perform aspects of the present invention.

Aspects of the present invention are described herein with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems), and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer readable program instructions.

These computer readable program instructions may be provided to aprocessor of a general purpose computer, special purpose computer, orother programmable data processing apparatus to produce a machine, suchthat the instructions, which execute via the processor of the computeror other programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks. These computer readable program instructionsmay also be stored in a computer readable storage medium that can directa computer, a programmable data processing apparatus, and/or otherdevices to function in a particular manner, such that the computerreadable storage medium having instructions stored therein comprises anarticle of manufacture including instructions which implement aspects ofthe function/act specified in the flowchart and/or block diagram blockor blocks.

The computer readable program instructions may also be loaded onto acomputer, other programmable data processing apparatus, or other deviceto cause a series of operational steps to be performed on the computer,other programmable apparatus or other device to produce a computerimplemented process, such that the instructions which execute on thecomputer, other programmable apparatus, or other device implement thefunctions/acts specified in the flowchart and/or block diagram block orblocks.

The flowchart and block diagrams in the Figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods, and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof instructions, which comprises one or more executable instructions forimplementing the specified logical function(s). In some alternativeimplementations, the functions noted in the block may occur out of theorder noted in the figures. For example, two blocks shown in successionmay, in fact, be executed substantially concurrently, or the blocks maysometimes be executed in the reverse order, depending upon thefunctionality involved. It will also be noted that each block of theblock diagrams and/or flowchart illustration, and combinations of blocksin the block diagrams and/or flowchart illustration, can be implementedby special purpose hardware-based systems that perform the specifiedfunctions or acts or carry out combinations of special purpose hardwareand computer instructions.

Reference in the specification to “one embodiment” or “an embodiment” ofthe present invention, as well as other variations thereof, means that aparticular feature, structure, characteristic, and so forth described inconnection with the embodiment is included in at least one embodiment ofthe present invention. Thus, the appearances of the phrase “in oneembodiment” or “in an embodiment”, as well any other variations,appearing in various places throughout the specification are notnecessarily all referring to the same embodiment.

It is to be appreciated that the use of any of the following “/”,“and/or”, and “at least one of”, for example, in the cases of “A/B”, “Aand/or B” and “at least one of A and B”, is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of both options (A andB). As a further example, in the cases of “A, B, and/or C” and “at leastone of A, B, and C”, such phrasing is intended to encompass theselection of the first listed option (A) only, or the selection of thesecond listed option (B) only, or the selection of the third listedoption (C) only, or the selection of the first and the second listedoptions (A and B) only, or the selection of the first and third listedoptions (A and C) only, or the selection of the second and third listedoptions (B and C) only, or the selection of all three options (A and Band C). This may be extended, as readily apparent by one of ordinaryskill in this and related arts, for as many items listed.

Having described preferred embodiments of a system and method (which areintended to be illustrative and not limiting), it is noted thatmodifications and variations can be made by persons skilled in the artin light of the above teachings. It is therefore to be understood thatchanges may be made in the particular embodiments disclosed which arewithin the scope of the invention as outlined by the appended claims.Having thus described aspects of the invention, with the details andparticularity required by the patent laws, what is claimed and desiredprotected by Letters Patent is set forth in the appended claims.

What is claimed is:
 1. A computer-implemented method for detecting object pose, comprising: training, by a processor, a first autoencoder (AE) to generate synthetic output images based on synthetic input images; training, by the processor, a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images; training, by the processor, a neural network (NN) to detect the object pose corresponding to a simulated environment using the synthetic output images generated by the first AE and the second AE; and detecting and outputting, by the processor, a pose of an object in a real input test image by inputting the real input test image to the second AE to generate a synthetic image therefrom, and then inputting the synthetic image to the NN to generate an NN output indicative of the pose of the object corresponding to an actual environment.
 2. The computer-implemented method of claim 1, wherein said first training step corresponds to synthetic-to-synthetic image learning, and said second training step corresponds to real-to-synthetic image learning.
 3. The computer-implemented method of claim 1, wherein during said second training step, a decoder of the second AE is fixed in order to generate the synthetic output images generated by the second AE to be similar to the synthetic output images generated by the first AE.
 4. The computer-implemented method of claim 1, further comprising configuring the first AE and the second AE to have respectively distinct encoding layers and common decoding layers.
 5. The computer-implemented method of claim 4, wherein the respectively distinct encoding layers in the first AE and the second AE are implemented by respective sets of one or more convolutional neural networks, and the common decoding layers are implemented by a set of one or more deconvolutional neural networks.
 6. The computer-implemented method of claim 4, wherein the NN is configured to detect the pose of the object based on a commonness between outputs of the common decoding layers of the first and second AEs.
 7. The computer-implemented method of claim 1, wherein the NN output comprises a first value corresponding to a first dimension and a second value corresponding to a second dimension.
 8. The computer-implemented method of claim 1, wherein the NN is a convolutional neural network.
 9. The computer-implemented method of claim 1, wherein the AE is a variational autoencoder.
 10. A non-transitory article of manufacture tangibly embodying a computer readable program which when executed causes a computer to perform the steps of claim
 1. 11. A computer-implemented method for detecting object pose, comprising: training, by a processor, a first autoencoder (AE) to generate synthetic output images based on synthetic input images; training, by the processor, a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images; training, by the processor, a multi-layer perceptron (MLP) to detect object pose corresponding to a simulated environment using only encoder outputs and bypassing decoder outputs of the first AE; and detecting and outputting, by the processor, a pose of an object in a real input test image by inputting the real input test image to an encoder of the second AE to generate an encoded synthetic image therefrom, and inputting the encoded synthetic image to the MLP to generate an MLP output indicative of the pose of the object corresponding to an actual environment.
 12. The computer-implemented method of claim 11, wherein said first training step corresponds to synthetic-to-synthetic image learning, and said second training step corresponds to real-to-synthetic image learning.
 13. The computer-implemented method of claim 11, wherein during said second training step, a decoder of the second AE is fixed in order to generate the synthetic output images generated by the second AE to be similar to the synthetic output images generated by the first AE.
 14. The computer-implemented method of claim 11, further comprising configuring the first AE and the second AE to have respectively distinct encoding layers and common decoding layers.
 15. The computer-implemented method of claim 14, wherein the respectively distinct encoding layers in the first AE and the second AE are implemented by respective sets of one or more convolutional neural networks, and the common decoding layers are implemented by a set of one or more deconvolutional neural networks
 16. The computer-implemented method of claim 11, wherein the MLP output comprises a first value corresponding to a first dimension and a second value corresponding to a second dimension.
 17. A non-transitory article of manufacture tangibly embodying a computer readable program which when executed causes a computer to perform the steps of claim
 1. 18. A system for detecting object pose, comprising: a processor, configured to train a first autoencoder (AE) to generate synthetic output images based on synthetic input images; train a second AE to generate synthetic output images, similar to the synthetic output images generated by the first AE, based on real input images; train a neural network (NN) to detect the object pose corresponding to a simulated environment using the synthetic output images generated by the first AE and the second AE; and detect and output a pose of an object in a real input test image by inputting the real input test image to the second AE to generate a synthetic image therefrom, and then inputting the synthetic image to the NN to generate an NN output indicative of the pose of the object corresponding to an actual environment.
 19. The system of claim 18, wherein the processor trains the second AE by fixing a decoder of the second AE in order to skew the synthetic output images generated by the second AE to be similar to the synthetic output images generated by the first AE.
 20. The system of claim 18, wherein the first AE and the second AE have respectively distinct encoding layers and common decoding layers. 